Supervisi Guru Berbasis AI di MTs Anwaha

Abstract

This study aims to analyze the implementation of artificial intelligence (AI)-based teacher supervision at MTs Anwaha, focusing on the monitoring of personalized learning, the detection of learning patterns and issues, and recommendations for teacher professional development. The background of this study is based on the continued dominance of conventional supervision practices that are administrative in nature and not yet fully data-driven. The method used is a qualitative approach with a case study design. Data were collected through observation, in-depth interviews, and documentation, then analyzed using the Miles and Huberman interactive model, which includes data reduction, data presentation, and drawing conclusions. The results of the study indicate that the integration of AI in supervision is capable of improving the quality of pedagogical decision-making in a more adaptive and evidence-based manner. In terms of personalized learning, AI helps supervisors and teachers understand the diversity of students’ needs more accurately. Regarding pattern detection, AI enables early identification of learning difficulties, allowing for preventive interventions. Meanwhile, in terms of professional development, AI generates more specific and contextual recommendations aligned with teachers’ actual classroom needs. The conclusion of this study affirms that AI-based supervision can transform supervisory practices to be more diagnostic, reflective, and focused on improving the quality of learning. The implications of this study highlight the importance of human resource readiness, institutional support, and the ethical use of data in optimizing the role of AI in educational supervision.